Business Intelligence Engineer, Amazon Customer Service

Job description

from Amazon careers

Amazon's Customer Service organization is seeking a Business Intelligence Engineer to join the Data Analytics Support Hub. Customer Service is the heart of Amazon. Our vision is to be Earth's most customer-centric company, and this team plays a central role in understanding why customers need to contact us and how we can prevent those contacts from happening in the first place.

The Advanced Analytics team is evolving Quality and Escalations analytics from descriptive reporting ("what happened") to diagnostic ("why did it happen) analytics at a worldwide level. We build the data infrastructure that powers multi-contact journey analysis, large-scale transcript processing, and machine-learning-driven root-cause identification across billions of customer interactions.

In this role, you will design and own the production data pipelines that turn domain expertise into scalable diagnostic insights. You will work across the full data lifecycle, from ingestion and modeling through certification and serving, enabling stakeholders to self-serve answers to complex "why" questions without waiting for ad-hoc analysis. Your work will directly feed Weekly Business Reviews, leadership dive-deeps, and the automation of diagnostic narratives using large language models.

This is an opportunity to combine deep data engineering craft with high-visibility business impact. You will operate at the intersection of large-scale data infrastructure and applied AI, shipping systems that meaningfully improve how Amazon understands and acts on customer experience signals worldwide.

Key job responsibilities
Build and own production data pipelines for diagnostic workloads: transcript ingestion at worldwide scale, multi-contact threading, journey-grain feature tables, and model-serving datasets.

Design and maintain end-to-end data models for the team's KPI portfolio, from raw source integration through consumption-ready tables.

Integrate team pipelines with central Customer Service data infrastructure, consuming shared tooling and contributing reusable components.

Scale innovations from analyst prototypes into maintainable, certified production pipelines with appropriate monitoring and alerting.

Build and maintain the transcript prototyping infrastructure used by stakeholders to self-serve, reducing time-to-delivery for new analytical requests.

Productionize LLM-based diagnostic outputs into reliable, refreshable datasets that power automated "why" narratives and self-service dive-deeps.

About the team
The Data Analytics Support Hub is a high-impact group responsible for diagnostic analytics across Amazon Customer Service worldwide. We answer the "why" behind quality and experience trends.

Our team combines business Analyst, data engineering, data science, and business intelligence expertise. We operate across the full stack: from raw transcript ingestion pipelines through feature engineering, LLM-based classification, and executive-facing automated narratives. We partner closely with operations, science, and product teams across CS to translate complex multi-interactions journey data into actionable insights that drive real improvements for customers.
Internal job description

Basic Qualifications

- Experience in analyzing and interpreting data with Redshift, Oracle, NoSQL etc.
- Experience with data visualization using Tableau, Quicksight, or similar tools
- Experience with data modeling, warehousing and building ETL pipelines
- Experience in Statistical Analysis packages such as R, SAS and Matlab
- Experience using SQL to pull data from a database or data warehouse and scripting experience (Python) to process data for modeling
- Experience with AWS solutions such as EC2, DynamoDB, S3, and Redshift
- Experience in data mining, ETL, etc. and using databases in a business environment with large-scale, complex datasets
- Master's degree in BI, finance, engineering, statistics, computer science, mathematics or equivalent quantitative field

Preferred Qualifications

- Experience Productionize LLM-based diagnostic outputs into reliable datasets

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